Visualization Analysis & Design
Full-Day Tutorial
Session 3
Tamara Munzner
Department of Computer Science
University of British Columbia
Sanger Institute / European Bioinformatics Institute
June 2014, Cambridge UK
http://www.cs.ubc.ca/~tmm/talks.html#minicours
e14
Outline
• Visualization Analysis Framework Session 1 9:30-10:45am
– Introduction: Definitions
– Analysis: What, Why, How
– Marks and Channels
• Idiom Design Choices, Part 2 Session 3 1:15pm-2:45pm
– Manipulate: Change, Select, Navigate
– Facet: Juxtapose, Partition, Superimpose
– Reduce: Filter, Aggregate, Embed
• Idiom Design Choices
Session 2 11:00am-12:15pm
– Arrange Tables
http://www.cs.ubc.ca/~tmm/talks.html#minicourse14
2
Idiom design choices: Part 1
3
Idiom design choices: Part 2
4
Manipulate
5
Change over time
• change any of the other choices
– encoding itself
– parameters
– arrange: rearrange, reorder
– aggregation level, what is filtered...
• why change?
– one of four major strategies
• change over time
• facet data by partitioning into multiple views
• reduce amount of data shown within view
– embedding focus + context together
– most obvious, powerful, flexible
– interaction entails change
6
Idiom: Re-encode
System: Tableau
made using Tableau, http://tableausoftware.com
7
Idiom: Reorder
System: LineUp
• data: tables with many attributes
• task: compare rankings
[LineUp: Visual Analysis of Multi-Attribute Rankings. Gratzl, Lex, Gehlenborg, Pfister, and Streit. IEEE Trans. Visualization and
Computer Graphics (Proc. InfoVis 2013) 19:12 (2013), 2277–2286.]
8
System: LineUp
Idiom: Realign
• stacked bars
– easy to compare
• first segment
• total bar
• align to different segment
– supports flexible comparison
[LineUp: Visual Analysis of Multi-Attribute Rankings.Gratzl, Lex, Gehlenborg,
Pfister, and Streit. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis
2013) 19:12 (2013), 2277–2286.]
9
Idiom: Animated transitions
• smooth transition from one state to another
– alternative to jump cuts
– support for item tracking when amount of change is limited
• example: multilevel matrix views
– scope of what is shown narrows down
• middle block stretches to fill space, additional structure appears within
• other blocks squish down to increasingly aggregated representations
[Using Multilevel Call Matrices in Large Software Projects. van Ham. Proc. IEEE Symp. Information Visualization (InfoVis), pp. 227–
232, 2003.]
10
Select and highlight
• selection: basic operation for most interaction
• design choices
– how many selection types?
• click vs hover: heavyweight, lightweight
• primary vs secondary: semantics (eg source/target)
• highlight: change visual encoding for selection targets
– color
• limitation: existing color coding hidden
– other channels (eg motion)
– add explicit connection marks between items
11
Navigate: Changing item visibility
• change viewpoint
– changes which items are visible within view
– camera metaphor
• zoom
– geometric zoom: familiar semantics
– semantic zoom: adapt object representation based on available pixels
» dramatic change, or more subtle one
• pan/translate
• rotate
– especially in 3D
– constrained navigation
• often with animated transitions
• often based on selection set
12
Idiom: Semantic zooming
System: LiveRAC
• visual encoding change
– colored box
– sparkline
– simple line chart
– full chart: axes and tickmarks
[LiveRAC - Interactive Visual Exploration of System Management Time-Series Data. McLachlan, Munzner, Koutsofios, and North.
Proc. ACM Conf. Human Factors in Computing Systems (CHI), pp. 1483–1492, 2008.]
13
Navigate: Reducing attributes
• continuation of camera
metaphor
– slice
• show only items matching specific
value for given attribute: slicing plane
• axis aligned, or arbitrary alignment
– cut
• show only items on far slide of plane
from camera
– project
• change mathematics of image
creation
– orthographic
[Interactive–Visualization
of Multimodal Volume Data for Neurosurgical Tumor Treatment. Rieder, Ritter, Raspe, and Peitgen.
perspective
Computer Graphics Forum (Proc. EuroVis 2008) 27:3 (2008), 1055–1062.]
14
Further reading
• Visualization Analysis and Design. Munzner. AK Peters / CRC Press,
Oct 2014.
– Chap 11: Manipulate View
• Animated Transitions in Statistical Data Graphics. Heer and Robertson.
IEEE Trans. on Visualization and Computer Graphics (Proc. InfoVis07)
13:6 (2007), 1240– 1247.
• Selection: 524,288 Ways to Say “This is Interesting”. Wills. Proc. IEEE
Symp. Information Visualization (InfoVis), pp. 54–61, 1996.
• Smooth and efficient zooming and panning. van Wijk and Nuij. Proc.
IEEE Symp. Information Visualization (InfoVis), pp. 15–22, 2003.
• Starting Simple - adding value to static visualisation through simple
interaction. Dix and Ellis. Proc. Advanced Visual Interfaces (AVI), pp.
124–134, 1998.
15
Outline
• Visualization Analysis Framework Session 1 9:30-10:45am
– Introduction: Definitions
– Analysis: What, Why, How
– Marks and Channels
• Idiom Design Choices, Part 2 Session 3 1:15pm-2:45pm
– Manipulate: Change, Select, Navigate
– Facet: Juxtapose, Partition, Superimpose
– Reduce: Filter, Aggregate, Embed
• Idiom Design Choices
Session 2 11:00am-12:15pm
– Arrange Tables
http://www.cs.ubc.ca/~tmm/talks.html#minicourse14
16
Facet
17
Juxtapose and coordinate views
19
Idiom: Linked highlighting
System: EDV
• see how regions
contiguous in one view
are distributed within
another
– powerful and
pervasive interaction
idiom
• encoding: different
– multiform
• data: all shared
[Visual Exploration of Large Structured Datasets. Wills. Proc. New
Techniques and Trends in Statistics (NTTS), pp. 237–246. IOS
Press, 1995.]
20
Idiom: bird’s-eye maps
System: Google Maps
• encoding: same
• data: subset shared
• navigation: shared
– bidirectional linking
• differences
– viewpoint
– (size)
• overview-detail
[A Review of Overview+Detail, Zooming, and Focus+Context
Interfaces. Cockburn, Karlson, and Bederson. ACM Computing
Surveys 41:1 (2008), 1–31.]
21
Idiom: Small multiples
• encoding: same
• data: none shared
System:
Cerebral
– different attributes for
node colors
– (same network
layout)
• navigation: shared
[Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context. Barsky, Munzner, Gardy, and
22
Kincaid. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2008) 14:6 (2008), 1253–1260.]
Coordinate views: Design choice interaction
23
Juxtapose design choices
• design choices
– view count
• few vs many
– how many is too many? open research question
– view visibility
• always side by side vs temporary popups
– view arrangement
• user managed vs system arranges/aligns
• why juxtapose views?
– benefits: eyes vs memory
• lower cognitive load to move eyes between 2 views than remembering previous state
with 1
– costs: display area
24
System: Improvise
• investigate power
of multiple views
– pushing limits on
view count,
interaction
complexity
– reorderable lists
• easy lookup
• useful when linked
to other encodings
[Building Highly-Coordinated Visualizations In Improvise. Weaver. Proc. IEEE
Symp. Information Visualization (InfoVis), pp. 159–166, 2004.]
25
Partition into views
• how to divide data between
views
– encodes association between items
using spatial proximity
– major implications for what patterns
are visible
– split according to attributes
• design choices
– how many splits
• all the way down: one mark per
region?
• stop earlier, for more complex
structure within region?
26
Views and glyphs
• view
– contiguous region in which visually
encoded data is shown on the
display
• glyph
– object with internal structure that
arises from multiple marks
• no strict dividing line
– view: big/detailed
– glyph:small/iconic
27
Partitioning: List alignment
• single bar chart with grouped bars
– split by state into regions
• complex glyph within each region showing all ages
– compare: easy within state, hard across ages
• small-multiple bar charts
– split by age into regions
• one chart per region
– compare: easy within age, harder across states
28
Partitioning: Recursive subdivision
System: HIVE
• split by type
• then by neighborhood
• then time
– years as rows
– months as columns
[Configuring Hierarchical Layouts to Address Research Questions. Slingsby, Dykes, and Wood. IEEE Transactions on Visualization
29
and Computer Graphics (Proc. InfoVis 2009) 15:6 (2009), 977–984.]
Partitioning: Recursive subdivision
System: HIVE
• switch order of splits
– neighborhood then type
• very different patterns
[Configuring Hierarchical Layouts to Address Research Questions. Slingsby, Dykes, and Wood. IEEE Transactions on Visualization
30
and Computer Graphics (Proc. InfoVis 2009) 15:6 (2009), 977–984.]
Partitioning: Recursive subdivision
System: HIVE
• size regions by sale
counts
– not uniformly
• result: treemap
[Configuring Hierarchical Layouts to Address Research Questions. Slingsby, Dykes, and Wood. IEEE Transactions on Visualization
31
and Computer Graphics (Proc. InfoVis 2009) 15:6 (2009), 977–984.]
Partitioning: Recursive subdivision
System: HIVE
• different encoding for
second-level regions
– choropleth maps
[Configuring Hierarchical Layouts to Address Research Questions. Slingsby, Dykes, and Wood. IEEE Transactions on Visualization
32
and Computer Graphics (Proc. InfoVis 2009) 15:6 (2009), 977–984.]
Superimpose layers
• layer: set of objects spread out over region
– each set is visually distinguishable group
– extent: whole view
• design choices
– how many layers?
– how are layers distinguished?
– small static set or dynamic from many possible?
– how partitioned?
• heavyweight with attribs vs lightweight with selection
• distinguishable layers
– encode with different, nonoverlapping channels
• two layers achieveable, three with careful design
33
Static visual layering
• foreground layer: roads
– hue, size distinguishing main from minor
– high luminance contrast from background
• background layer: regions
– desaturated colors for water, parks, land
areas
• user can selectively focus attention
• “get it right in black and white”
– check luminance contrast with greyscale
view
[Get it right in black and white. Stone. 2010.
http://www.stonesc.com/wordpress/2010/03/get-it-right-in-blackand-white]
34
Superimposing limits
• few layers, but many lines
– up to a few dozen
– but not hundreds
• superimpose vs juxtapose: empirical study
– superimposed for local visual, multiple for global
– same screen space for all multiples, single
superimposed
– tasks
• local: maximum, global: slope, discrimination
[Graphical Perception of Multiple Time
Series. Javed, McDonnel, and Elmqvist.
IEEE Transactions on Visualization and
Computer Graphics (Proc. IEEE InfoVis
2010) 16:6 (2010), 927–934.]
35
Dynamic visual layering
• interactive, from
selection
System:
Cerebral
– lightweight: click
– very lightweight: hover
• ex: 1-hop neighbors
[Cerebral: a Cytoscape plugin for layout of
and interaction with biological networks using
subcellular localization annotation. Barsky,
Gardy, Hancock, and Munzner. Bioinformatics
36
Further reading
• Visualization Analysis and Design. Munzner. AK Peters / CRC Press, Oct 2014.
– Chap 12: Facet Into Multiple Views
• A Review of Overview+Detail, Zooming, and Focus+Context Interfaces. Cockburn, Karlson, and Bederson. ACM
Computing Surveys 41:1 (2008), 1–31.
• A Guide to Visual Multi-Level Interface Design From Synthesis of Empirical Study Evidence. Lam and Munzner.
Synthesis Lectures on Visualization Series, Morgan Claypool, 2010.
• Zooming versus multiple window interfaces: Cognitive costs of visual comparisons. Plumlee and Ware. ACM Trans. on
Computer-Human Interaction (ToCHI) 13:2 (2006), 179–209.
• Exploring the Design Space of Composite Visualization. Javed and Elmqvist. Proc. Pacific Visualization Symp.
(PacificVis), pp. 1–9, 2012.
• Visual Comparison for Information Visualization. Gleicher, Albers, Walker, Jusufi, Hansen, and Roberts. Information
Visualization 10:4 (2011), 289–309.
• Guidelines for Using Multiple Views in Information Visualizations. Baldonado, Woodruff, and Kuchinsky. In Proc. ACM
Advanced Visual Interfaces (AVI), pp. 110–119, 2000.
• Cross-Filtered Views for Multidimensional Visual Analysis. Weaver. IEEE Trans. Visualization and Computer Graphics
16:2 (Proc. InfoVis 2010), 192–204, 2010.
• Linked Data Views. Wills. In Handbook of Data Visualization, Computational Statistics, edited by Unwin, Chen, and
Härdle, pp. 216–241. Springer-Verlag, 2008.
• Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications. Borgo, Kehrer, Chung,
Maguire, Laramee, Hauser, Ward, and Chen. In Eurographics State of the Art Reports, pp. 39–63, 2013.
37
Outline
• Visualization Analysis Framework Session 1 9:30-10:45am
– Introduction: Definitions
– Analysis: What, Why, How
– Marks and Channels
• Idiom Design Choices, Part 2 Session 3 1:15pm-2:45pm
– Manipulate: Change, Select, Navigate
– Facet: Juxtapose, Partition, Superimpose
– Reduce: Filter, Aggregate, Embed
• Idiom Design Choices
Session 2 11:00am-12:15pm
– Arrange Tables
http://www.cs.ubc.ca/~tmm/talks.html#minicourse14
38
Reduce items and attributes
• reduce/increase: inverses
• filter
– pro: straightforward and intuitive
• to understand and compute
– con: out of sight, out of mind
• aggregation
– pro: inform about whole set
– con: difficult to avoid losing signal
• not mutually exclusive
– combine filter, aggregate
– combine reduce, change, facet
39
Idiom: dynamic filtering
System: FilmFinder
• item filtering
• browse through tightly coupled interaction
– alternative to queries that might return far too many or too few
[Visual information seeking: Tight coupling of dynamic query filters with starfield displays. Ahlberg and
Shneiderman. Proc. ACM Conf. on Human Factors in Computing Systems (CHI), pp. 313–317, 1994.]
40
Idiom: scented widgets
• augment widgets for filtering to show information scent
– cues to show whether value in drilling down further vs looking elsewhere
• concise, in part of screen normally considered control panel
[Scented Widgets: Improving Navigation Cues with Embedded Visualizations. Willett, Heer, and
Agrawala. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2007) 13:6 (2007), 1129–
1136.]
41
Idiom: DOSFA
• attribute filtering
• encoding: star glyphs
[Interactive Hierarchical Dimension Ordering, Spacing and Filtering for Exploration Of High
Dimensional Datasets. Yang, Peng,Ward, and. Rundensteiner. Proc. IEEE Symp. Information
Visualization (InfoVis), pp. 105–112, 2003.]
42
Idiom: histogram
•
•
•
•
static item aggregation
task: find distribution
data: table
derived data
– new table: keys are bins, values are counts
• bin size crucial
– pattern can change dramatically depending on discretization
– opportunity for interaction: control bin size on the fly
43
Idiom: boxplot
•
•
•
•
static item aggregation
task: find distribution
data: table
derived data
– 5 quant attribs
• median: central line
• lower and upper quartile: boxes
• lower upper fences: whiskers
– values beyond which items are outliers
– outliers beyond fence cutoffs explicitly shown
[40 years of boxplots. Wickham and Stryjewski. 2012. had.co.nz]
44
Idiom: Hierarchical parallel coordinates
• dynamic item aggregation
• derived data: hierarchical clustering
• encoding:
– cluster band with variable transparency, line at mean, width by min/max
values
– color by proximity in hierarchy
[Hierarchical Parallel Coordinates for Exploration of Large Datasets. Fua, Ward, and
Rundensteiner. Proc. IEEE Visualization Conference (Vis ’99), pp. 43– 50, 1999.]
45
Dimensionality reduction
• attribute aggregation
– derive low-dimensional target space from high-dimensional measured space
– use when you can’t directly measure what you care about
• true dimensionality of dataset conjectured to be smaller than dimensionality of
measurements
• latent factors, hidden variables
Malignant
Benign
Tumor
Measurement Data
DR
data: 9D measured space
derived data: 2D target
space
46
46
Dimensionality reduction for documents
47
Embed: Focus+Context
• combine information within
single view
• elide
– selectively filter and
aggregate
• superimpose layer
– local lens
• distortion design choices
– region shape: radial,
rectilinear, complex
– how many regions: one,
many
49
Idiom: DOITrees Revisited
• elide
– some items dynamically filtered out
– some items dynamically aggregated together
– some items shown in detail
50
[DOITrees Revisited: Scalable, Space-Constrained Visualization of Hierarchical Data. Heer and Card. Proc. Advanced Visual Interfaces (AVI)
Idiom: Fisheye Lens
• distort geometry
– shape: radial
– focus: single extent
– extent: local
– metaphor: draggable lens
http://tulip.labri.fr/TulipDrupal/?q=node/351
http://tulip.labri.fr/TulipDrupal/?q=node/371
51
Idiom: Stretch and Squish Navigation
• distort geometry
– shape: rectilinear
System:
– foci: multiple
– impact: global
– metaphor: stretch and squish, borders fixed
TreeJuxtaposer
[TreeJuxtaposer: Scalable Tree Comparison Using Focus+Context With Guaranteed Visibility. Munzner, Guimbretiere,
Tasiran, Zhang, and Zhou. ACM Transactions on Graphics (Proc. SIGGRAPH) 22:3 (2003), 453– 462.]
52
Distortion costs and benefits
fisheye lens
magnifying lens
neighborhood layering
Bring and Go
• benefits
– combine focus and context
information in single view
• costs
– length comparisons impaired
• network/tree topology
comparisons unaffected:
connection, containment
– effects of distortion unclear if
original structure unfamiliar
– object constancy/tracking
maybe impaired
[Living Flows: Enhanced Exploration of Edge-Bundled Graphs Based on GPU-Intensive Edge Rendering. Lambert, Auber, and
Melançon. Proc. Intl. Conf. Information Visualisation (IV), pp. 523–530, 2010.]
53
Further reading
• Visualization Analysis and Design. Munzner. AK Peters / CRC Press,
Oct 2014.
– Chap 14: Embed: Focus+Context
• A Review of Overview+Detail, Zooming, and Focus+Context Interfaces.
Cockburn, Karlson, and Bederson. ACM Computing Surveys 41:1
(2008), 1–31.
• A Guide to Visual Multi-Level Interface Design From Synthesis of
Empirical Study Evidence. Lam and Munzner. Synthesis Lectures on
Visualization Series, Morgan Claypool, 2010.
• Hierarchical Aggregation for Information Visualization: Overview,
Techniques and Design Guidelines. Elmqvist and Fekete. IEEE
Transactions on Visualization and Computer Graphics 16:3 (2010),
439–454.
54